DESCRIPTION (Adapted from the Principal Investigator's Abstract): In this application the principal investigator proposes to further develop sophisticated modeling methodologies that will aid investigators with model-building and model-validation techniques. In their previous work, the PI and his co-investigators have centered their attention on the development of statistical methods for describing pharmacokinetic (PK) and pharmacodynamic (PD) data, mainly as implemented with the computer program NONMEM. The current proposal describes work that will go beyond the planned enhancements to NONMEM to develop entirely new data modeling tools. These will better meet the requirements of investigators with PK/PD data of forms not easily modeled with current software and that will allow investigators to infer and test more mechanistic models. Specifically, they plan to: (1) Further develop nonparametric and semi-parametric modes for PK/PD data analysis by seeking models for complex nonlinear systems and extending them to a population context. Such techniques will describe data using a minimum of assumptions for initial model-building explorations. (2) Evaluate the performance of sophisticated vs simpler approaches to complex systems analysis in situations in which the data are low information, such as when the data are especially sparse and the population variability is difficult to resolve between inter-and intra-subject components and when the PD data are categorical rather than continuous and a multivariate analysis is still desired. (3) Evaluate the "posterior predictive check" approach to model validation in population PK/PD so that it can be determined whether the estimated parameters are derived from well- or poorly-specified models of the random effects.